Overview

Dataset statistics

Number of variables23
Number of observations3272774
Missing cells8510964
Missing cells (%)11.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory574.3 MiB
Average record size in memory184.0 B

Variable types

Numeric13
Categorical10

Alerts

time has a high cardinality: 3256632 distinct values High cardinality
id has a high cardinality: 3256955 distinct values High cardinality
updated has a high cardinality: 3124771 distinct values High cardinality
place has a high cardinality: 205436 distinct values High cardinality
locationSource has a high cardinality: 199 distinct values High cardinality
magSource has a high cardinality: 333 distinct values High cardinality
latitude is highly correlated with longitudeHigh correlation
longitude is highly correlated with latitudeHigh correlation
depth is highly correlated with rmsHigh correlation
mag is highly correlated with rmsHigh correlation
nst is highly correlated with horizontalErrorHigh correlation
gap is highly correlated with horizontalErrorHigh correlation
dmin is highly correlated with rmsHigh correlation
rms is highly correlated with depth and 2 other fieldsHigh correlation
horizontalError is highly correlated with nst and 2 other fieldsHigh correlation
depthError is highly correlated with horizontalErrorHigh correlation
latitude is highly correlated with magHigh correlation
longitude is highly correlated with magHigh correlation
mag is highly correlated with latitude and 2 other fieldsHigh correlation
nst is highly correlated with magNstHigh correlation
rms is highly correlated with magHigh correlation
magNst is highly correlated with nstHigh correlation
horizontalError is highly correlated with depthErrorHigh correlation
depthError is highly correlated with horizontalErrorHigh correlation
latitude is highly correlated with longitude and 3 other fieldsHigh correlation
longitude is highly correlated with latitude and 3 other fieldsHigh correlation
depth is highly correlated with latitudeHigh correlation
mag is highly correlated with longitude and 2 other fieldsHigh correlation
magType is highly correlated with latitude and 4 other fieldsHigh correlation
nst is highly correlated with magTypeHigh correlation
net is highly correlated with latitude and 3 other fieldsHigh correlation
mag has 156449 (4.8%) missing values Missing
magType has 167407 (5.1%) missing values Missing
nst has 881566 (26.9%) missing values Missing
gap has 838549 (25.6%) missing values Missing
dmin has 1346742 (41.1%) missing values Missing
rms has 211653 (6.5%) missing values Missing
horizontalError has 1531963 (46.8%) missing values Missing
depthError has 606685 (18.5%) missing values Missing
magError has 1781012 (54.4%) missing values Missing
magNst has 988917 (30.2%) missing values Missing
rms is highly skewed (γ1 = 23.86159713) Skewed
depthError is highly skewed (γ1 = 1354.147975) Skewed
Unnamed: 0 is uniformly distributed Uniform
time is uniformly distributed Uniform
id is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
depth has 49534 (1.5%) zeros Zeros
nst has 329596 (10.1%) zeros Zeros
depthError has 68134 (2.1%) zeros Zeros
magError has 35432 (1.1%) zeros Zeros
magNst has 107921 (3.3%) zeros Zeros

Reproduction

Analysis started2022-03-14 19:49:20.447952
Analysis finished2022-03-14 20:00:39.774304
Duration11 minutes and 19.33 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct3272774
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1636386.5
Minimum0
Maximum3272773
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.0 MiB
2022-03-14T21:00:39.995902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile163638.65
Q1818193.25
median1636386.5
Q32454579.75
95-th percentile3109134.35
Maximum3272773
Range3272773
Interquartile range (IQR)1636386.5

Descriptive statistics

Standard deviation944768.6193
Coefficient of variation (CV)0.5773505338
Kurtosis-1.2
Mean1636386.5
Median Absolute Deviation (MAD)818193.5
Skewness-3.0727145 × 10-17
Sum5.355523191 × 1012
Variance8.92587744 × 1011
MonotonicityNot monotonic
2022-03-14T21:00:40.182766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9884811
 
< 0.1%
21425621
 
< 0.1%
21425601
 
< 0.1%
21425591
 
< 0.1%
21425581
 
< 0.1%
21425571
 
< 0.1%
21425561
 
< 0.1%
21425551
 
< 0.1%
21425541
 
< 0.1%
21425531
 
< 0.1%
Other values (3272764)3272764
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
32727731
< 0.1%
32727721
< 0.1%
32727711
< 0.1%
32727701
< 0.1%
32727691
< 0.1%
32727681
< 0.1%
32727671
< 0.1%
32727661
< 0.1%
32727651
< 0.1%
32727641
< 0.1%

time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3256632
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size25.0 MiB
1970-01-01T00:00:00.0Z
 
7
2014-03-02T11:31:33.000Z
 
4
1999-11-30T00:00:00.000Z
 
3
1986-03-02T18:05:37.900Z
 
2
1986-03-02T16:52:58.480Z
 
2
Other values (3256627)
3272756 

Length

Max length24
Median length24
Mean length23.99999572
Min length22

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3240498 ?
Unique (%)99.0%

Sample

1st row1970-01-01T00:00:00.0Z
2nd row1970-01-01T00:00:00.0Z
3rd row1970-01-01T00:00:00.0Z
4th row1970-01-01T00:00:00.0Z
5th row1970-01-01T00:00:00.0Z

Common Values

ValueCountFrequency (%)
1970-01-01T00:00:00.0Z7
 
< 0.1%
2014-03-02T11:31:33.000Z4
 
< 0.1%
1999-11-30T00:00:00.000Z3
 
< 0.1%
1986-03-02T18:05:37.900Z2
 
< 0.1%
1986-03-02T16:52:58.480Z2
 
< 0.1%
1986-03-02T16:57:44.390Z2
 
< 0.1%
1986-03-02T16:57:58.300Z2
 
< 0.1%
1986-03-02T17:00:48.180Z2
 
< 0.1%
1986-03-02T17:01:29.620Z2
 
< 0.1%
1986-03-02T17:11:02.620Z2
 
< 0.1%
Other values (3256622)3272746
> 99.9%

Length

2022-03-14T21:00:40.853836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1970-01-01t00:00:00.0z7
 
< 0.1%
2014-03-02t11:31:33.000z4
 
< 0.1%
1999-11-30t00:00:00.000z3
 
< 0.1%
2005-03-02t06:25:32.970z2
 
< 0.1%
2005-03-02t06:04:36.700z2
 
< 0.1%
2005-03-02t06:10:13.700z2
 
< 0.1%
2005-03-02t06:11:26.820z2
 
< 0.1%
2005-03-02t06:13:31.430z2
 
< 0.1%
2005-03-02t06:16:24.750z2
 
< 0.1%
2005-03-02t06:34:03.210z2
 
< 0.1%
Other values (3256622)3272746
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

latitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct419761
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.72073813
Minimum-84.422
Maximum87.265
Zeros10
Zeros (%)< 0.1%
Negative250887
Negative (%)7.7%
Memory size25.0 MiB
2022-03-14T21:00:41.110038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-84.422
5-th percentile-13.18035
Q134.118
median37.5761667
Q342.2586667
95-th percentile63.0834
Maximum87.265
Range171.687
Interquartile range (IQR)8.1406667

Descriptive statistics

Standard deviation20.25672335
Coefficient of variation (CV)0.5670857998
Kurtosis4.418748879
Mean35.72073813
Median Absolute Deviation (MAD)3.5581667
Skewness-1.792298533
Sum116905903
Variance410.334841
MonotonicityNot monotonic
2022-03-14T21:00:41.280857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.032978
 
0.1%
34.622126
 
0.1%
33.061531
 
< 0.1%
38.82166671243
 
< 0.1%
38.81951110
 
< 0.1%
38.8201667985
 
< 0.1%
38.8218333982
 
< 0.1%
34.35969
 
< 0.1%
38.8146667954
 
< 0.1%
38.8198333949
 
< 0.1%
Other values (419751)3258947
99.6%
ValueCountFrequency (%)
-84.4221
< 0.1%
-84.1331
< 0.1%
-83.9021
< 0.1%
-82.0641
< 0.1%
-81.171
< 0.1%
-80.7321
< 0.1%
-79.98371
< 0.1%
-77.361
< 0.1%
-77.1341
< 0.1%
-77.081
< 0.1%
ValueCountFrequency (%)
87.2651
< 0.1%
87.2211
< 0.1%
87.1611
< 0.1%
87.1181
< 0.1%
87.0921
< 0.1%
87.0811
< 0.1%
87.0681
< 0.1%
87.0511
< 0.1%
87.0091
< 0.1%
87.0081
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct610455
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-92.85667108
Minimum-179.999
Maximum180
Zeros17
Zeros (%)< 0.1%
Negative2825927
Negative (%)86.3%
Memory size25.0 MiB
2022-03-14T21:00:41.477206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-179.999
5-th percentile-155.2777
Q1-122.7958298
median-118.8111667
Q3-115.4541667
95-th percentile130.252
Maximum180
Range359.999
Interquartile range (IQR)7.3416631

Descriptive statistics

Standard deviation80.55325537
Coefficient of variation (CV)-0.8675010038
Kurtosis3.377257838
Mean-92.85667108
Median Absolute Deviation (MAD)3.9753333
Skewness2.10801072
Sum-303898898.8
Variance6488.82695
MonotonicityNot monotonic
2022-03-14T21:00:41.648165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-117.683070
 
0.1%
-117.31542
 
< 0.1%
-1151444
 
< 0.1%
-116.85886
 
< 0.1%
-118.28843
 
< 0.1%
-122.7963333818
 
< 0.1%
-122.8103333811
 
< 0.1%
-122.796726
 
< 0.1%
-122.7971667709
 
< 0.1%
-122.7975708
 
< 0.1%
Other values (610445)3261217
99.6%
ValueCountFrequency (%)
-179.9996
< 0.1%
-179.99891
 
< 0.1%
-179.99861
 
< 0.1%
-179.99851
 
< 0.1%
-179.99832
 
< 0.1%
-179.99821
 
< 0.1%
-179.9983
< 0.1%
-179.99781
 
< 0.1%
-179.99761
 
< 0.1%
-179.99721
 
< 0.1%
ValueCountFrequency (%)
18011
< 0.1%
179.99991
 
< 0.1%
179.99932
 
< 0.1%
179.99911
< 0.1%
179.99881
 
< 0.1%
179.9989
< 0.1%
179.99792
 
< 0.1%
179.99771
 
< 0.1%
179.99762
 
< 0.1%
179.99751
 
< 0.1%

depth
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct60221
Distinct (%)1.8%
Missing9
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean22.33494593
Minimum-10
Maximum735.8
Zeros49534
Zeros (%)1.5%
Negative180678
Negative (%)5.5%
Memory size25.0 MiB
2022-03-14T21:00:41.833573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile-0.159
Q13.002
median7.155
Q315
95-th percentile100
Maximum735.8
Range745.8
Interquartile range (IQR)11.998

Descriptive statistics

Standard deviation56.32032848
Coefficient of variation (CV)2.521623677
Kurtosis55.47258837
Mean22.33494593
Median Absolute Deviation (MAD)4.874
Skewness6.66499493
Sum73097029.32
Variance3171.9794
MonotonicityNot monotonic
2022-03-14T21:00:42.013036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10182559
 
5.6%
33104344
 
3.2%
049534
 
1.5%
539471
 
1.2%
3520224
 
0.6%
116608
 
0.5%
614270
 
0.4%
157076
 
0.2%
26808
 
0.2%
306743
 
0.2%
Other values (60211)2825128
86.3%
ValueCountFrequency (%)
-102
 
< 0.1%
-9.92
 
< 0.1%
-6.81
 
< 0.1%
-6.61
 
< 0.1%
-5.62
 
< 0.1%
-5.31
 
< 0.1%
-5.21
 
< 0.1%
-4.910
< 0.1%
-4.86
< 0.1%
-4.71
 
< 0.1%
ValueCountFrequency (%)
735.81
< 0.1%
728.51
< 0.1%
721.81
< 0.1%
7171
< 0.1%
712.51
< 0.1%
712.21
< 0.1%
709.71
< 0.1%
707.91
< 0.1%
707.31
< 0.1%
700.91
< 0.1%

mag
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct751
Distinct (%)< 0.1%
Missing156449
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean1.878940887
Minimum-9.99
Maximum9.1
Zeros22265
Zeros (%)0.7%
Negative62366
Negative (%)1.9%
Memory size25.0 MiB
2022-03-14T21:00:42.202269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9.99
5-th percentile0.26
Q10.97
median1.5
Q32.46
95-th percentile4.6
Maximum9.1
Range19.09
Interquartile range (IQR)1.49

Descriptive statistics

Standard deviation1.352506451
Coefficient of variation (CV)0.7198238437
Kurtosis1.44343478
Mean1.878940887
Median Absolute Deviation (MAD)0.66
Skewness0.7949535801
Sum5855390.46
Variance1.829273701
MonotonicityNot monotonic
2022-03-14T21:00:42.379338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.465660
 
2.0%
1.364188
 
2.0%
1.161198
 
1.9%
1.260503
 
1.8%
1.557333
 
1.8%
1.655779
 
1.7%
152448
 
1.6%
1.747642
 
1.5%
0.946583
 
1.4%
1.844462
 
1.4%
Other values (741)2560529
78.2%
(Missing)156449
 
4.8%
ValueCountFrequency (%)
-9.99678
< 0.1%
-51
 
< 0.1%
-31
 
< 0.1%
-2.62
 
< 0.1%
-2.54
 
< 0.1%
-2.21
 
< 0.1%
-24
 
< 0.1%
-1.95
 
< 0.1%
-1.85
 
< 0.1%
-1.761
 
< 0.1%
ValueCountFrequency (%)
9.12
 
< 0.1%
8.81
 
< 0.1%
8.62
 
< 0.1%
8.42
 
< 0.1%
8.36
 
< 0.1%
8.27
 
< 0.1%
8.16
 
< 0.1%
812
< 0.1%
7.921
< 0.1%
7.827
< 0.1%

magType
Categorical

HIGH CORRELATION
MISSING

Distinct30
Distinct (%)< 0.1%
Missing167407
Missing (%)5.1%
Memory size25.0 MiB
md
1137605 
ml
1092572 
mb
376129 
mc
248196 
mh
113933 
Other values (25)
136932 

Length

Max length7
Median length2
Mean length2.024506282
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowmh
2nd rowmh
3rd rowmh
4th rowmh
5th rowmh

Common Values

ValueCountFrequency (%)
md1137605
34.8%
ml1092572
33.4%
mb376129
 
11.5%
mc248196
 
7.6%
mh113933
 
3.5%
Ml21334
 
0.7%
mdl20303
 
0.6%
mwc20081
 
0.6%
Md17242
 
0.5%
mw12187
 
0.4%
Other values (20)45785
 
1.4%
(Missing)167407
 
5.1%

Length

2022-03-14T21:00:42.558014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
md1154847
37.2%
ml1113906
35.9%
mb376158
 
12.1%
mc248196
 
8.0%
mh113933
 
3.7%
mdl20303
 
0.7%
mwc20081
 
0.6%
mw12188
 
0.4%
mblg11782
 
0.4%
m8506
 
0.3%
Other values (15)25467
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nst
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct739
Distinct (%)< 0.1%
Missing881566
Missing (%)26.9%
Infinite0
Infinite (%)0.0%
Mean15.6014964
Minimum0
Maximum934
Zeros329596
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size25.0 MiB
2022-03-14T21:00:42.716525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q318
95-th percentile45
Maximum934
Range934
Interquartile range (IQR)13

Descriptive statistics

Standard deviation26.60686592
Coefficient of variation (CV)1.705404741
Kurtosis146.7141195
Mean15.6014964
Median Absolute Deviation (MAD)5
Skewness9.603187493
Sum37306423
Variance707.9253143
MonotonicityNot monotonic
2022-03-14T21:00:42.887279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0329596
 
10.1%
6151910
 
4.6%
7150554
 
4.6%
5140938
 
4.3%
8137374
 
4.2%
9123345
 
3.8%
10109294
 
3.3%
4108227
 
3.3%
1197062
 
3.0%
1285305
 
2.6%
Other values (729)957603
29.3%
(Missing)881566
26.9%
ValueCountFrequency (%)
0329596
10.1%
162
 
< 0.1%
21930
 
0.1%
329005
 
0.9%
4108227
 
3.3%
5140938
4.3%
6151910
4.6%
7150554
4.6%
8137374
4.2%
9123345
 
3.8%
ValueCountFrequency (%)
9341
< 0.1%
9291
< 0.1%
9181
< 0.1%
8851
< 0.1%
8821
< 0.1%
8621
< 0.1%
8571
< 0.1%
8321
< 0.1%
8211
< 0.1%
8141
< 0.1%

gap
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct30876
Distinct (%)1.3%
Missing838549
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean130.4876076
Minimum0
Maximum360
Zeros13825
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size25.0 MiB
2022-03-14T21:00:43.071197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.5
Q179
median115
Q3168.26
95-th percentile274
Maximum360
Range360
Interquartile range (IQR)89.26

Descriptive statistics

Standard deviation69.71062107
Coefficient of variation (CV)0.5342317355
Kurtosis0.2893891101
Mean130.4876076
Median Absolute Deviation (MAD)42
Skewness0.888355514
Sum317636196.6
Variance4859.57069
MonotonicityNot monotonic
2022-03-14T21:00:43.248673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013825
 
0.4%
10013496
 
0.4%
7713314
 
0.4%
7613250
 
0.4%
9813160
 
0.4%
7513147
 
0.4%
7413131
 
0.4%
7813121
 
0.4%
7213032
 
0.4%
8012998
 
0.4%
Other values (30866)2301751
70.3%
(Missing)838549
 
25.6%
ValueCountFrequency (%)
013825
0.4%
6.51
 
< 0.1%
72
 
< 0.1%
84
 
< 0.1%
8.41
 
< 0.1%
8.51
 
< 0.1%
8.61
 
< 0.1%
8.71
 
< 0.1%
911
 
< 0.1%
9.31
 
< 0.1%
ValueCountFrequency (%)
360661
< 0.1%
359.914
 
< 0.1%
359.813
 
< 0.1%
359.77
 
< 0.1%
359.66
 
< 0.1%
359.54
 
< 0.1%
359.33
 
< 0.1%
359.21
 
< 0.1%
359.12
 
< 0.1%
35966
 
< 0.1%

dmin
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct43434
Distinct (%)2.3%
Missing1346742
Missing (%)41.1%
Infinite0
Infinite (%)0.0%
Mean0.2559989956
Minimum0
Maximum141.16
Zeros785
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.0 MiB
2022-03-14T21:00:43.443600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.006306
Q10.02093
median0.05135
Q30.116
95-th percentile0.739245
Maximum141.16
Range141.16
Interquartile range (IQR)0.09507

Descriptive statistics

Standard deviation1.333459187
Coefficient of variation (CV)5.208845383
Kurtosis680.1915783
Mean0.2559989956
Median Absolute Deviation (MAD)0.03694
Skewness19.22444355
Sum493062.2576
Variance1.778113402
MonotonicityNot monotonic
2022-03-14T21:00:43.628634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0099121373
 
0.7%
0.0108120984
 
0.6%
0.00900920297
 
0.6%
0.0117120159
 
0.6%
0.0126120028
 
0.6%
0.0135118703
 
0.6%
0.00810818662
 
0.6%
0.0144118091
 
0.6%
0.0153217525
 
0.5%
0.00720717048
 
0.5%
Other values (43424)1733162
53.0%
(Missing)1346742
41.1%
ValueCountFrequency (%)
0785
< 0.1%
0.00010661
 
< 0.1%
0.00012121
 
< 0.1%
0.00012571
 
< 0.1%
0.00012811
 
< 0.1%
0.00012936
 
< 0.1%
0.00013173
 
< 0.1%
0.00013251
 
< 0.1%
0.00014121
 
< 0.1%
0.00014141
 
< 0.1%
ValueCountFrequency (%)
141.161
< 0.1%
134.431
< 0.1%
127.421
< 0.1%
122.811
< 0.1%
114.611
< 0.1%
111.51
< 0.1%
102.91
< 0.1%
87.4481
< 0.1%
87.0871
< 0.1%
79.921
< 0.1%

rms
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct4842
Distinct (%)0.2%
Missing211653
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean0.3152052393
Minimum-1
Maximum104.33
Zeros32506
Zeros (%)1.0%
Negative3
Negative (%)< 0.1%
Memory size25.0 MiB
2022-03-14T21:00:43.974663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.02
Q10.06
median0.15
Q30.48
95-th percentile1.1
Maximum104.33
Range105.33
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.3999009455
Coefficient of variation (CV)1.268700185
Kurtosis3866.23352
Mean0.3152052393
Median Absolute Deviation (MAD)0.11
Skewness23.86159713
Sum964881.3773
Variance0.1599207662
MonotonicityNot monotonic
2022-03-14T21:00:44.154719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03132882
 
4.1%
0.02129834
 
4.0%
0.04125220
 
3.8%
0.05114615
 
3.5%
0.01109658
 
3.4%
0.06102436
 
3.1%
0.0783660
 
2.6%
0.0865875
 
2.0%
0.0955662
 
1.7%
0.153451
 
1.6%
Other values (4832)2087828
63.8%
(Missing)211653
 
6.5%
ValueCountFrequency (%)
-13
 
< 0.1%
032506
1.0%
0.0001135
 
< 0.1%
0.000282
 
< 0.1%
0.000377
 
< 0.1%
0.000467
 
< 0.1%
0.000560
 
< 0.1%
0.000659
 
< 0.1%
0.000753
 
< 0.1%
0.000854
 
< 0.1%
ValueCountFrequency (%)
104.331
< 0.1%
88.391
< 0.1%
71.451
< 0.1%
69.321
< 0.1%
64.291
< 0.1%
54.641
< 0.1%
53.971
< 0.1%
50.51
< 0.1%
44.511
< 0.1%
44.081
< 0.1%

net
Categorical

HIGH CORRELATION

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.0 MiB
nc
893352 
us
742356 
ci
687371 
ak
468147 
nn
188158 
Other values (15)
293390 

Length

Max length10
Median length2
Mean length2.010014746
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowci
2nd rowci
3rd rowci
4th rowci
5th rowci

Common Values

ValueCountFrequency (%)
nc893352
27.3%
us742356
22.7%
ci687371
21.0%
ak468147
14.3%
nn188158
 
5.7%
uw117553
 
3.6%
uu97105
 
3.0%
hv24196
 
0.7%
pr19491
 
0.6%
mb11176
 
0.3%
Other values (10)23869
 
0.7%

Length

2022-03-14T21:00:44.327018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nc893352
27.3%
us742356
22.7%
ci687371
21.0%
ak468147
14.3%
nn188158
 
5.7%
uw117553
 
3.6%
uu97105
 
3.0%
hv24196
 
0.7%
pr19491
 
0.6%
mb11176
 
0.3%
Other values (10)23869
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3256955
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size25.0 MiB
ci15471369
 
2
ci117296
 
2
usp0002reb
 
2
nc1136373
 
2
nc10084145
 
2
Other values (3256950)
3272764 

Length

Max length28
Median length10
Mean length9.831384935
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3241136 ?
Unique (%)99.0%

Sample

1st rowci37038459
2nd rowci11092098
3rd rowci15086796
4th rowci14891508
5th rowci10925125

Common Values

ValueCountFrequency (%)
ci154713692
 
< 0.1%
ci1172962
 
< 0.1%
usp0002reb2
 
< 0.1%
nc11363732
 
< 0.1%
nc100841452
 
< 0.1%
nc100841472
 
< 0.1%
nc11363742
 
< 0.1%
usp0002rec2
 
< 0.1%
usp0002red2
 
< 0.1%
usp0002ree2
 
< 0.1%
Other values (3256945)3272754
> 99.9%

Length

2022-03-14T21:00:44.691491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ci154713692
 
< 0.1%
nc214426442
 
< 0.1%
ak0052rhz3922
 
< 0.1%
usp000dha62
 
< 0.1%
nc214426362
 
< 0.1%
uw106422832
 
< 0.1%
ak0052rj9oda2
 
< 0.1%
nc690253522
 
< 0.1%
ak0052rjr4uv2
 
< 0.1%
ak0052rkdx7a2
 
< 0.1%
Other values (3256945)3272754
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

updated
Categorical

HIGH CARDINALITY

Distinct3124771
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size25.0 MiB
2018-06-04T20:43:45.000Z
 
434
2015-05-13T18:53:08.000Z
 
244
2015-05-13T18:53:10.000Z
 
241
2015-05-13T18:53:07.000Z
 
241
2015-05-13T18:53:06.000Z
 
230
Other values (3124766)
3271384 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3097344 ?
Unique (%)94.6%

Sample

1st row2016-04-02T20:22:05.312Z
2nd row2016-01-29T01:43:14.870Z
3rd row2016-04-02T17:20:31.235Z
4th row2016-04-02T14:10:48.389Z
5th row2016-04-02T04:32:22.103Z

Common Values

ValueCountFrequency (%)
2018-06-04T20:43:45.000Z434
 
< 0.1%
2015-05-13T18:53:08.000Z244
 
< 0.1%
2015-05-13T18:53:10.000Z241
 
< 0.1%
2015-05-13T18:53:07.000Z241
 
< 0.1%
2015-05-13T18:53:06.000Z230
 
< 0.1%
2015-05-13T18:53:11.000Z229
 
< 0.1%
2015-05-13T18:53:04.000Z227
 
< 0.1%
2015-05-13T18:53:05.000Z220
 
< 0.1%
2015-05-13T18:53:09.000Z211
 
< 0.1%
2015-05-13T18:53:12.000Z190
 
< 0.1%
Other values (3124761)3270307
99.9%

Length

2022-03-14T21:00:45.074526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-06-04t20:43:45.000z434
 
< 0.1%
2015-05-13t18:53:08.000z244
 
< 0.1%
2015-05-13t18:53:10.000z241
 
< 0.1%
2015-05-13t18:53:07.000z241
 
< 0.1%
2015-05-13t18:53:06.000z230
 
< 0.1%
2015-05-13t18:53:11.000z229
 
< 0.1%
2015-05-13t18:53:04.000z227
 
< 0.1%
2015-05-13t18:53:05.000z220
 
< 0.1%
2015-05-13t18:53:09.000z211
 
< 0.1%
2015-05-13t18:53:12.000z190
 
< 0.1%
Other values (3124761)3270307
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

place
Categorical

HIGH CARDINALITY

Distinct205436
Distinct (%)6.3%
Missing11
Missing (%)< 0.1%
Memory size25.0 MiB
Northern California
356578 
Central California
277140 
Central Alaska
 
119754
Nevada
 
102096
Long Valley area, California
 
88181
Other values (205431)
2329014 

Length

Max length85
Median length23
Mean length23.1504475
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116913 ?
Unique (%)3.6%

Sample

1st row29km NE of Independence, CA
2nd row11km SSW of Lake Nacimiento, CA
3rd row4km S of La Canada Flintridge, CA
4th row9km S of Cabazon, CA
5th row5km SE of Niland, CA

Common Values

ValueCountFrequency (%)
Northern California356578
 
10.9%
Central California277140
 
8.5%
Central Alaska119754
 
3.7%
Nevada102096
 
3.1%
Long Valley area, California88181
 
2.7%
Southern Alaska73156
 
2.2%
Utah42467
 
1.3%
Washington40075
 
1.2%
San Francisco Bay area, California28558
 
0.9%
Mount St. Helens area, Washington28209
 
0.9%
Other values (205426)2116549
64.7%

Length

2022-03-14T21:00:45.305486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of1416287
 
11.5%
california1124265
 
9.1%
alaska529172
 
4.3%
ca449325
 
3.7%
northern414497
 
3.4%
central411498
 
3.3%
islands209457
 
1.7%
area160219
 
1.3%
valley152568
 
1.2%
region151823
 
1.2%
Other values (9694)7276540
59.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

type
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.0 MiB
earthquake
3184269 
quarry blast
 
60898
explosion
 
20133
ice quake
 
3951
mining explosion
 
1240
Other values (20)
 
2283

Length

Max length26
Median length10
Mean length10.03489425
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowsonic boom
2nd rowearthquake
3rd rowearthquake
4th rowsonic boom
5th rowsonic boom

Common Values

ValueCountFrequency (%)
earthquake3184269
97.3%
quarry blast60898
 
1.9%
explosion20133
 
0.6%
ice quake3951
 
0.1%
mining explosion1240
 
< 0.1%
nuclear explosion738
 
< 0.1%
other event517
 
< 0.1%
chemical explosion392
 
< 0.1%
sonic boom351
 
< 0.1%
rock burst180
 
< 0.1%
Other values (15)105
 
< 0.1%

Length

2022-03-14T21:00:45.479865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
earthquake3184269
95.3%
quarry60915
 
1.8%
blast60898
 
1.8%
explosion22508
 
0.7%
ice3951
 
0.1%
quake3951
 
0.1%
mining1240
 
< 0.1%
nuclear738
 
< 0.1%
event518
 
< 0.1%
other517
 
< 0.1%
Other values (24)1603
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

horizontalError
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8725
Distinct (%)0.5%
Missing1531963
Missing (%)46.8%
Infinite0
Infinite (%)0.0%
Mean1.266841139
Minimum0
Maximum280.6
Zeros30
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.0 MiB
2022-03-14T21:00:45.636085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16
Q10.3
median0.48
Q30.93
95-th percentile5.85
Maximum280.6
Range280.6
Interquartile range (IQR)0.63

Descriptive statistics

Standard deviation3.168282045
Coefficient of variation (CV)2.500930817
Kurtosis421.7268834
Mean1.266841139
Median Absolute Deviation (MAD)0.23
Skewness15.20937124
Sum2205330.991
Variance10.03801112
MonotonicityNot monotonic
2022-03-14T21:00:45.804683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.346545
 
1.4%
0.240313
 
1.2%
0.437721
 
1.2%
0.527889
 
0.9%
0.2625706
 
0.8%
0.2725643
 
0.8%
0.2525527
 
0.8%
0.2825332
 
0.8%
0.2925312
 
0.8%
0.2424808
 
0.8%
Other values (8715)1436015
43.9%
(Missing)1531963
46.8%
ValueCountFrequency (%)
030
 
< 0.1%
0.0143426
0.1%
0.0221360
 
< 0.1%
0.0281642
0.1%
0.032204
 
< 0.1%
0.0361234
 
< 0.1%
0.04177
 
< 0.1%
0.042852
 
< 0.1%
0.045387
 
< 0.1%
0.05893
 
< 0.1%
ValueCountFrequency (%)
280.61
< 0.1%
194.58411
< 0.1%
131.81
< 0.1%
114.2311
< 0.1%
109.91
< 0.1%
103.5351
< 0.1%
103.2141
< 0.1%
102.5791
< 0.1%
100.9131
< 0.1%
99.9711
< 0.1%

depthError
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct26851
Distinct (%)1.0%
Missing606685
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean5.640319809
Minimum-1
Maximum1773552.5
Zeros68134
Zeros (%)2.1%
Negative3
Negative (%)< 0.1%
Memory size25.0 MiB
2022-03-14T21:00:45.993651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.1
Q10.49
median0.96
Q32.76
95-th percentile18
Maximum1773552.5
Range1773553.5
Interquartile range (IQR)2.27

Descriptive statistics

Standard deviation1167.801181
Coefficient of variation (CV)207.0452067
Kurtosis2008980.549
Mean5.640319809
Median Absolute Deviation (MAD)0.65
Skewness1354.147975
Sum15037594.6
Variance1363759.597
MonotonicityNot monotonic
2022-03-14T21:00:46.169857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
068134
 
2.1%
31.6161773
 
1.9%
0.357859
 
1.8%
0.256341
 
1.7%
0.452905
 
1.6%
0.150868
 
1.6%
0.546286
 
1.4%
0.639211
 
1.2%
0.732668
 
1.0%
0.827205
 
0.8%
Other values (26841)2172839
66.4%
(Missing)606685
 
18.5%
ValueCountFrequency (%)
-13
 
< 0.1%
068134
2.1%
0.00187
 
< 0.1%
0.00257
 
< 0.1%
0.003403
 
< 0.1%
0.004185
 
< 0.1%
0.005176
 
< 0.1%
0.006214
 
< 0.1%
0.007226
 
< 0.1%
0.008219
 
< 0.1%
ValueCountFrequency (%)
1773552.51
< 0.1%
456553.81
< 0.1%
367558.11
< 0.1%
263163.61
< 0.1%
148928.81
< 0.1%
143094.41
< 0.1%
104046.31
< 0.1%
58249.31
< 0.1%
37882.71
< 0.1%
36544.41
< 0.1%

magError
Real number (ℝ≥0)

MISSING
ZEROS

Distinct2304
Distinct (%)0.2%
Missing1781012
Missing (%)54.4%
Infinite0
Infinite (%)0.0%
Mean0.1668926841
Minimum0
Maximum6.11
Zeros35432
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size25.0 MiB
2022-03-14T21:00:46.360920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.029
Q10.09
median0.142
Q30.21
95-th percentile0.38
Maximum6.11
Range6.11
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.1474292975
Coefficient of variation (CV)0.8833778326
Kurtosis200.33969
Mean0.1668926841
Median Absolute Deviation (MAD)0.061
Skewness8.78860057
Sum248964.1642
Variance0.02173539777
MonotonicityNot monotonic
2022-03-14T21:00:46.530119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.140759
 
1.2%
0.1239202
 
1.2%
0.1338825
 
1.2%
0.1138164
 
1.2%
0.0936845
 
1.1%
0.1436448
 
1.1%
0.0436331
 
1.1%
035432
 
1.1%
0.1534598
 
1.1%
0.0834566
 
1.1%
Other values (2294)1120592
34.2%
(Missing)1781012
54.4%
ValueCountFrequency (%)
035432
1.1%
0.001253
 
< 0.1%
0.002255
 
< 0.1%
0.003249
 
< 0.1%
0.0035030610671
 
< 0.1%
0.004263
 
< 0.1%
0.0041806687081
 
< 0.1%
0.005264
 
< 0.1%
0.006305
 
< 0.1%
0.007342
 
< 0.1%
ValueCountFrequency (%)
6.111
< 0.1%
5.991
< 0.1%
5.981
< 0.1%
5.921
< 0.1%
5.911
< 0.1%
5.91
< 0.1%
5.721
< 0.1%
5.671
< 0.1%
5.621
< 0.1%
5.611
< 0.1%

magNst
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct572
Distinct (%)< 0.1%
Missing988917
Missing (%)30.2%
Infinite0
Infinite (%)0.0%
Mean12.6073861
Minimum0
Maximum941
Zeros107921
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size25.0 MiB
2022-03-14T21:00:46.715241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median7
Q314
95-th percentile43
Maximum941
Range941
Interquartile range (IQR)11

Descriptive statistics

Standard deviation21.12723866
Coefficient of variation (CV)1.675782632
Kurtosis104.7922671
Mean12.6073861
Median Absolute Deviation (MAD)4
Skewness7.556319532
Sum28793467
Variance446.3602133
MonotonicityNot monotonic
2022-03-14T21:00:46.886959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3192369
 
5.9%
4182457
 
5.6%
5171848
 
5.3%
2166911
 
5.1%
6153345
 
4.7%
1138702
 
4.2%
7128834
 
3.9%
8109463
 
3.3%
0107921
 
3.3%
990053
 
2.8%
Other values (562)841954
25.7%
(Missing)988917
30.2%
ValueCountFrequency (%)
0107921
3.3%
1138702
4.2%
2166911
5.1%
3192369
5.9%
4182457
5.6%
5171848
5.3%
6153345
4.7%
7128834
3.9%
8109463
3.3%
990053
2.8%
ValueCountFrequency (%)
9411
< 0.1%
8541
< 0.1%
8211
< 0.1%
8001
< 0.1%
7901
< 0.1%
7741
< 0.1%
7521
< 0.1%
6841
< 0.1%
6791
< 0.1%
6671
< 0.1%

status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size25.0 MiB
reviewed
3133150 
automatic
 
139611
manual
 
12

Length

Max length9
Median length8
Mean length8.042650987
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreviewed
2nd rowreviewed
3rd rowreviewed
4th rowreviewed
5th rowreviewed

Common Values

ValueCountFrequency (%)
reviewed3133150
95.7%
automatic139611
 
4.3%
manual12
 
< 0.1%
(Missing)1
 
< 0.1%

Length

2022-03-14T21:00:47.054386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-14T21:00:47.151805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
reviewed3133150
95.7%
automatic139611
 
4.3%
manual12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

locationSource
Categorical

HIGH CARDINALITY

Distinct199
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.0 MiB
nc
893621 
ci
687375 
us
561882 
ak
475199 
nn
188165 
Other values (194)
466532 

Length

Max length10
Median length2
Mean length2.067562869
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st rowci
2nd rowci
3rd rowci
4th rowci
5th rowci

Common Values

ValueCountFrequency (%)
nc893621
27.3%
ci687375
21.0%
us561882
17.2%
ak475199
14.5%
nn188165
 
5.7%
uw117562
 
3.6%
uu97113
 
3.0%
hv24253
 
0.7%
guc23866
 
0.7%
aeic21140
 
0.6%
Other values (189)182598
 
5.6%

Length

2022-03-14T21:00:47.252569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nc893621
27.3%
ci687375
21.0%
us561882
17.2%
ak475199
14.5%
nn188165
 
5.7%
uw117562
 
3.6%
uu97113
 
3.0%
hv24253
 
0.7%
guc23866
 
0.7%
aeic21140
 
0.6%
Other values (189)182598
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

magSource
Categorical

HIGH CARDINALITY

Distinct333
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.0 MiB
nc
894014 
ci
687379 
us
480643 
ak
476650 
nn
188176 
Other values (328)
545912 

Length

Max length10
Median length2
Mean length2.094228932
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)< 0.1%

Sample

1st rowci
2nd rowci
3rd rowci
4th rowci
5th rowci

Common Values

ValueCountFrequency (%)
nc894014
27.3%
ci687379
21.0%
us480643
14.7%
ak476650
14.6%
nn188176
 
5.7%
uw117570
 
3.6%
uu97117
 
3.0%
ath25601
 
0.8%
hv24258
 
0.7%
guc20898
 
0.6%
Other values (323)260468
 
8.0%

Length

2022-03-14T21:00:47.533007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nc894014
27.3%
ci687379
21.0%
us480643
14.7%
ak476650
14.6%
nn188176
 
5.7%
uw117570
 
3.6%
uu97117
 
3.0%
ath25601
 
0.8%
hv24258
 
0.7%
guc20898
 
0.6%
Other values (320)260468
 
8.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-14T20:59:25.974286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:12.766904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:24.444886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:38.706372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:51.127987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:03.481172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:15.487492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:25.936223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:36.582412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:46.120736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:57.201123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:06.341131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:17.487139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:26.812448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:13.779056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:25.408268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:39.736222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:52.278264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:04.580832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:16.342289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:26.851802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:37.348015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:47.088544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:57.881391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:07.268576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:18.119144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:27.620178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:14.724525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:26.436022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:40.689144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:53.404933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:05.655831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:17.188638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:27.739141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:38.051456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:48.054470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:58.581232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:08.159479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:18.746702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:28.433663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:15.761232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:27.463881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:41.727367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:54.439335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:06.727192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:18.028578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:28.626730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:38.746558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:49.011063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:59.255262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:09.098149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:19.359019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:29.245837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:16.782270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:28.507252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:43.074584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:55.478346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:07.781210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:18.837439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:29.500758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:39.440718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:49.934893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:59.910555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:10.237813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:19.975445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:30.012573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:17.625991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:29.387384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:43.932663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:56.325668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:08.680604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:19.649942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:30.364807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:40.116020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:50.714094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:00.544710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:11.135347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:20.560128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:30.785181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:18.485491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:30.279092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:44.809727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:57.173816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:09.589746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:20.435925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:31.253245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:40.850984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:51.518433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:01.212976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:12.057430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:21.190951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:31.460240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:19.216569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:31.036912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:45.556342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:57.902836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:10.331083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:21.169913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:32.002094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:41.531132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:52.245389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:01.856283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:12.901430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:21.777701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:32.303568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:20.266043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:32.152612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:46.635165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:58.999266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:11.462456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:21.993156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:32.878232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:42.261647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:53.416029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:02.567531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:13.864575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:22.462137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:32.951499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:20.929803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:32.862157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:47.343627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:59.699977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:12.168982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:22.639787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:33.544955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:42.877035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:54.110127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:03.331028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:14.537198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:23.021269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:33.704185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:21.828531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:33.828065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:48.362564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:00.613779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:13.126614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:23.417637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:34.410345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:43.588553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:55.022385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:03.979979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:15.472475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:23.649236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:34.329008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:22.492997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:34.506760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:49.050799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:01.292742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:13.785244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:24.033211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:35.062233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:44.209414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:55.681551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:04.545650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:16.100395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:24.285545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:35.155730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:23.343543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:37.617150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:57:49.962915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:02.334764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:14.635834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:24.987288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:35.872914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:45.072557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:58:56.549793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:05.195046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:16.845731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-14T20:59:24.910795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-14T21:00:47.666754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-14T21:00:47.898148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-14T21:00:48.140228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-14T21:00:48.350033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-03-14T21:00:48.515178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-14T20:59:42.382672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-14T20:59:53.902474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-14T21:00:16.194236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-14T21:00:23.881469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0timelatitudelongitudedepthmagmagTypenstgapdminrmsnetidupdatedplacetypehorizontalErrordepthErrormagErrormagNststatuslocationSourcemagSource
09884811970-01-01T00:00:00.0Z37.003502-117.9968340.000.00mh0.0NaNNaNNaNcici370384592016-04-02T20:22:05.312Z29km NE of Independence, CAsonic boomNaNNaNNaN0.0reviewedcici
19884801970-01-01T00:00:00.0Z35.642788-120.9336015.001.99mh2.0NaNNaNNaNcici110920982016-01-29T01:43:14.870Z11km SSW of Lake Nacimiento, CAearthquakeNaNNaNNaN0.0reviewedcici
29884781970-01-01T00:00:00.0Z34.164520-118.1850360.000.00mhNaNNaNNaNNaNcici150867962016-04-02T17:20:31.235Z4km S of La Canada Flintridge, CAearthquakeNaNNaNNaN0.0reviewedcici
39884771970-01-01T00:00:00.0Z33.836494-116.7818680.000.00mhNaNNaNNaNNaNcici148915082016-04-02T14:10:48.389Z9km S of Cabazon, CAsonic boomNaNNaNNaN0.0reviewedcici
49884761970-01-01T00:00:00.0Z33.208477-115.4769975.000.00mhNaNNaNNaNNaNcici109251252016-04-02T04:32:22.103Z5km SE of Niland, CAsonic boomNaNNaNNaN0.0reviewedcici
59884751970-01-01T00:00:00.0Z32.663559-116.1050260.000.00mh0.0NaNNaNNaNcici150992282016-04-02T14:10:49.670Z13km SW of Ocotillo, CAsonic boomNaNNaNNaN0.0reviewedcici
69884791970-01-01T00:00:00.0Z35.354450-115.4848940.000.50mh0.0NaNNaNNaNcici101699022016-01-29T01:19:00.110Z30km SSW of Primm, NVquarry blastNaNNaNNaN0.0reviewedcici
79884741970-01-01T06:16:08.780Z46.276500-118.360000-0.262.30md6.0303.00.52000.14uwuw108360082016-07-24T23:40:13.450ZWashingtonearthquake0.4100.240.1200.0revieweduwuw
89884731970-01-01T06:44:28.060Z46.332833-118.391167-0.262.60md6.0299.00.48140.15uwuw108360132016-07-24T23:40:13.680ZWashingtonearthquake0.1440.090.2100.0revieweduwuw
99884721970-01-01T15:13:21.040Z32.707167-115.4170006.002.75mh4.0214.00.60360.58cici33249512016-01-29T01:29:00.290Z8km NE of Mexicali, B.C., MXearthquake4.60031.610.0846.0reviewedcici

Last rows

Unnamed: 0timelatitudelongitudedepthmagmagTypenstgapdminrmsnetidupdatedplacetypehorizontalErrordepthErrormagErrormagNststatuslocationSourcemagSource
327276421620452019-03-29T20:36:05.380Z36.754300-115.0105009.301.30ml6.0245.890.767000.28nnnn006811062019-03-29T20:37:34.766Z38km WNW of Moapa Town, NevadaearthquakeNaN24.60NaNNaNautomaticnnnn
327276521620442019-03-29T20:40:12.492Z63.048200-150.53360030.601.60mlNaNNaNNaN1.26akak01941vft5t2019-03-29T20:46:08.286Z83km NNW of Talkeetna, AlaskaearthquakeNaN1.40NaNNaNautomaticakak
327276621620432019-03-29T20:55:42.570Z30.614800131.02310026.755.20mwwNaN65.002.505000.97usus2000k7dg2019-03-29T21:13:01.040Z13km S of Nishinoomote, Japanearthquake6.004.600.07119.0reviewedusus
327276721620422019-03-29T20:59:19.740Z36.978500-121.6374975.181.81md17.085.000.050090.06ncnc731582502019-03-29T21:27:04.645Z7km WSW of Gilroy, CAearthquake0.270.520.19021.0automaticncnc
327276821620412019-03-29T21:18:45.410Z37.219833-121.7611698.731.56md8.0104.000.068730.04ncnc731582652019-03-29T21:49:02.766Z10km SE of Seven Trees, CAearthquake0.411.400.1907.0automaticncnc
327276921620402019-03-29T21:22:47.574Z61.417200-147.56490013.101.20mlNaNNaNNaN0.66akak01941vxgl12019-03-29T21:27:36.099Z72km WNW of Valdez, AlaskaearthquakeNaN0.40NaNNaNautomaticakak
327277021620392019-03-29T21:31:26.830Z66.227700-157.2026000.001.80mlNaNNaNNaN1.01akak01941vzces2019-03-29T21:36:48.145Z77km S of Kobuk, AlaskaearthquakeNaN0.40NaNNaNautomaticakak
327277121620382019-03-29T21:33:07.890Z33.234667-116.77116712.450.65ml19.054.000.010480.16cici375966022019-03-29T21:36:32.434Z1km SW of Lake Henshaw, CAearthquake0.270.700.15814.0automaticcici
327277221620372019-03-29T21:41:40.340Z62.829900-148.76640055.501.80mlNaNNaNNaN0.51akak01941w1iml2019-03-29T21:46:40.652Z63km S of Cantwell, AlaskaearthquakeNaN1.60NaNNaNautomaticakak
327277321620362019-03-29T21:47:56.920Z33.036333-116.4360005.481.19ml39.066.000.142800.23cici375966422019-03-29T21:51:39.630Z16km ESE of Julian, CAearthquake0.331.640.14228.0automaticcici